




'''simple docstring'''




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_ :						Tuple				 =			"""true"""





def 	a_				( __snake_case				:     Optional[int]  ,			__snake_case				:     Any=82  ,			__snake_case				:     Optional[Any]=16  )				->		Optional[Any]:



       """simple docstring"""
       set_seed(42  )
       lowerCamelCase_				=RegressionModel()
       lowerCamelCase_				=deepcopy(__snake_case  )
       lowerCamelCase_				=RegressionDataset(length=__snake_case  )
       lowerCamelCase_				=DataLoader(__snake_case  ,			batch_size=__snake_case  )
       model.to(accelerator.device  )
       lowerCamelCase_, lowerCamelCase_				=accelerator.prepare(__snake_case  ,			__snake_case  )
       return model, ddp_model, dataloader





def 	a_				( __snake_case				:     Any  ,			__snake_case				:     Dict=False  )				->		Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_				=AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''  )
       lowerCamelCase_				=load_dataset('''glue'''  ,			'''mrpc'''  ,			split='''validation'''  )

       def tokenize_function(__snake_case				:     int  ):
              lowerCamelCase_				=tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=__snake_case  ,			max_length=__snake_case  )
              return outputs

       with accelerator.main_process_first():
              lowerCamelCase_				=dataset.map(
                  __snake_case  ,			batched=__snake_case  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			)

       lowerCamelCase_				=tokenized_datasets.rename_column('''label'''  ,			'''labels'''  )

       def collate_fn(__snake_case				:     Optional[Any]  ):
              if use_longest:
                     return tokenizer.pad(__snake_case  ,			padding='''longest'''  ,			return_tensors='''pt'''  )
              return tokenizer.pad(__snake_case  ,			padding='''max_length'''  ,			max_length=128  ,			return_tensors='''pt'''  )

       return DataLoader(__snake_case  ,			shuffle=__snake_case  ,			collate_fn=__snake_case  ,			batch_size=16  )





def 	a_				( __snake_case				:     Tuple  ,			__snake_case				:     List[str]  )				->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_				=Accelerator(dispatch_batches=__snake_case  ,			split_batches=__snake_case  )
       lowerCamelCase_				=get_dataloader(__snake_case  ,			not dispatch_batches  )
       lowerCamelCase_				=AutoModelForSequenceClassification.from_pretrained(
           '''hf-internal-testing/mrpc-bert-base-cased'''  ,			return_dict=__snake_case  )
       lowerCamelCase_, lowerCamelCase_				=accelerator.prepare(__snake_case  ,			__snake_case  )
       return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 	a_				( __snake_case				:     List[Any]  ,			__snake_case				:     List[str]  ,			__snake_case				:     Any  )				->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_				=[]
       for batch in dataloader:
              lowerCamelCase_, lowerCamelCase_				=batch.values()
              with torch.no_grad():
                     lowerCamelCase_				=model(__snake_case  )
                     lowerCamelCase_, lowerCamelCase_				=accelerator.gather_for_metrics((logit, target)  )
                     logits_and_targets.append((logit, target)  )
       lowerCamelCase_, lowerCamelCase_				=[], []
       for logit, targ in logits_and_targets:
              logits.append(__snake_case  )
              targs.append(__snake_case  )
       lowerCamelCase_, lowerCamelCase_				=torch.cat(__snake_case  ), torch.cat(__snake_case  )
       return logits, targs





def 	a_				( __snake_case				:     Union[str, Any]  ,			__snake_case				:     int=82  ,			__snake_case				:     List[Any]=False  ,			__snake_case				:     int=False  ,			__snake_case				:     Any=16  )				->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_, lowerCamelCase_, lowerCamelCase_				=get_basic_setup(__snake_case  ,			__snake_case  ,			__snake_case  )
       lowerCamelCase_, lowerCamelCase_				=generate_predictions(__snake_case  ,			__snake_case  ,			__snake_case  )
       assert (
           len(__snake_case  ) == num_samples
       ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__snake_case  )}'''





def 	a_				( __snake_case				:     List[str] = False  ,			__snake_case				:     Any = False  )				->		Tuple:



       """simple docstring"""
       lowerCamelCase_				=evaluate.load('''glue'''  ,			'''mrpc'''  )
       lowerCamelCase_, lowerCamelCase_				=get_mrpc_setup(__snake_case  ,			__snake_case  )
       # First do baseline
       lowerCamelCase_, lowerCamelCase_, lowerCamelCase_				=setup['''no''']
       model.to(__snake_case  )
       model.eval()
       for batch in dataloader:
              batch.to(__snake_case  )
              with torch.inference_mode():
                     lowerCamelCase_				=model(**__snake_case  )
              lowerCamelCase_				=outputs.logits.argmax(dim=-1  )
              metric.add_batch(predictions=__snake_case  ,			references=batch['''labels''']  )
       lowerCamelCase_				=metric.compute()

       # Then do distributed
       lowerCamelCase_, lowerCamelCase_, lowerCamelCase_				=setup['''ddp''']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     lowerCamelCase_				=model(**__snake_case  )
              lowerCamelCase_				=outputs.logits.argmax(dim=-1  )
              lowerCamelCase_				=batch['''labels''']
              lowerCamelCase_, lowerCamelCase_				=accelerator.gather_for_metrics((preds, references)  )
              metric.add_batch(predictions=__snake_case  ,			references=__snake_case  )
       lowerCamelCase_				=metric.compute()

       for key in "accuracy f1".split():
              assert math.isclose(
                  baseline[key]  ,			distributed[key]  ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''





def 	a_				( )				->		List[Any]:



       """simple docstring"""
       lowerCamelCase_				=Accelerator(split_batches=__snake_case  ,			dispatch_batches=__snake_case  )
       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_warning()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
       # These are a bit slower so they should only be ran on the GPU or TPU
       if torch.cuda.is_available() or is_tpu_available():
              if accelerator.is_local_main_process:
                     print('''**Testing gather_for_metrics**'''  )
              for split_batches in [True, False]:
                     for dispatch_batches in [True, False]:
                            if accelerator.is_local_main_process:
                                   print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''  )
                            test_mrpc(__snake_case  ,			__snake_case  )
                            accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('''**Test torch metrics**'''  )
       for split_batches in [True, False]:
              for dispatch_batches in [True, False]:
                     lowerCamelCase_				=Accelerator(split_batches=__snake_case  ,			dispatch_batches=__snake_case  )
                     if accelerator.is_local_main_process:
                            print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''  )
                     test_torch_metrics(__snake_case  ,			99  )
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('''**Test last batch is not dropped when perfectly divisible**'''  )
       lowerCamelCase_				=Accelerator()
       test_torch_metrics(__snake_case  ,			512  )
       accelerator.state._reset_state()





def 	a_				( __snake_case				:     Any  )				->		str:



       """simple docstring"""
       main()


if __name__ == "__main__":
     main()










'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)

@dataclass
class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Union[List[PIL.Image.Image], np.ndarray]
 __snake_case:							Optional[List[bool]]
 __snake_case:							Optional[List[bool]]


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker


